29 games have been completed at #RWC2019 (link). I have continued with my idea of characterising the games played with a single number (link). This number is the median ratio of kicks and passes divided by lineouts and scrums. My hope is that this expresses the mobility of each game.
I am using data form the official World Rugby website (link) to curate my data for the tournament. I have noted that these numbers do change after the games have played (particularly the number of passes). I have used data twenty four hours after the completion of the game as a record of that game. I am collecting the data as a Google Sheet (link) with tabs for each game played.
I have used ggplot to visualise data and I am using the data to help me improve my use of R. These visualistions include:
- a colour blind palette (link)
My visualisations of the 29 games are including identified outliers are:
I have a Ratio for each game. The tournament median is 2.31 and is expressed by a geom_hline default size and shape in black.
The Ratio is expressed with a geom-smooth function in order to see what the trends in the data look like. The confidence limits are set by default at 95%. I have used the loess method with my small number of data points. The grey area expresses the confidence band for the regression line drawn with the method. The confidence interval can be turned of with se = FALSE or set at a level you specify:
Passes with a geom_hline set at the median number of passes (258):
Passes with a geom-smooth:
Kicks during the game with geom_hline set at a median set at 58 kicks per game:
Kicks with geom_smooth:
Penalties and Free Kicks Conceded with geom_point with a geom_hline set at a median of 16 penalties and free kicks conceded per game:
Penalties and free kicks conceded presented with a geom_smooth function:
Lineouts and scrums have medians of 25 and 13 respectively per game:
Lineouts and scrums with a geom_smooth:
Lineout Win (World Rugby)